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patch: deprecate (a)get_relevant_documents (#20477)

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- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
ccurme
2024-04-22 11:14:53 -04:00
committed by GitHub
parent 939d113d10
commit c010ec8b71
171 changed files with 443 additions and 535 deletions
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4
cookbook/Multi_modal_RAG.ipynb
View File

@@ -604,7 +604,7 @@
"source": [
"# Check retrieval\n",
"query = \"Give me company names that are interesting investments based on EV / NTM and NTM rev growth. Consider EV / NTM multiples vs historical?\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query, limit=6)\n",
"docs = retriever_multi_vector_img.invoke(query, limit=6)\n",
"\n",
"# We get 4 docs\n",
"len(docs)"
@@ -630,7 +630,7 @@
"source": [
"# Check retrieval\n",
"query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query, limit=6)\n",
"docs = retriever_multi_vector_img.invoke(query, limit=6)\n",
"\n",
"# We get 4 docs\n",
"len(docs)"

2
cookbook/Multi_modal_RAG_google.ipynb
View File

@@ -604,7 +604,7 @@
],
"source": [
"query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query, limit=1)\n",
"docs = retriever_multi_vector_img.invoke(query, limit=1)\n",
"\n",
"# We get 2 docs\n",
"len(docs)"

8
cookbook/Semi_structured_and_multi_modal_RAG.ipynb
View File

@@ -562,9 +562,7 @@
],
"source": [
"# We can retrieve this table\n",
"retriever.get_relevant_documents(\n",
" \"What are results for LLaMA across across domains / subjects?\"\n",
")[1]"
"retriever.invoke(\"What are results for LLaMA across across domains / subjects?\")[1]"
]
},
{
@@ -614,9 +612,7 @@
}
],
"source": [
"retriever.get_relevant_documents(\"Images / figures with playful and creative examples\")[\n",
" 1\n",
"]"
"retriever.invoke(\"Images / figures with playful and creative examples\")[1]"
]
},
{

4
cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb
View File

@@ -501,9 +501,7 @@
}
],
"source": [
"retriever.get_relevant_documents(\"Images / figures with playful and creative examples\")[\n",
" 0\n",
"]"
"retriever.invoke(\"Images / figures with playful and creative examples\")[0]"
]
},
{

2
cookbook/advanced_rag_eval.ipynb
View File

@@ -342,7 +342,7 @@
"# Testing on retrieval\n",
"query = \"What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?\"\n",
"suffix_for_images = \" Include any pie charts, graphs, or tables.\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query + suffix_for_images)"
"docs = retriever_multi_vector_img.invoke(query + suffix_for_images)"
]
},
{

2
cookbook/custom_agent_with_plugin_retrieval.ipynb
View File

@@ -169,7 +169,7 @@
"\n",
"def get_tools(query):\n",
" # Get documents, which contain the Plugins to use\n",
" docs = retriever.get_relevant_documents(query)\n",
" docs = retriever.invoke(query)\n",
" # Get the toolkits, one for each plugin\n",
" tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
" # Get the tools: a separate NLAChain for each endpoint\n",

2
cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb
View File

@@ -193,7 +193,7 @@
"\n",
"def get_tools(query):\n",
" # Get documents, which contain the Plugins to use\n",
" docs = retriever.get_relevant_documents(query)\n",
" docs = retriever.invoke(query)\n",
" # Get the toolkits, one for each plugin\n",
" tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
" # Get the tools: a separate NLAChain for each endpoint\n",

2
cookbook/custom_agent_with_tool_retrieval.ipynb
View File

@@ -142,7 +142,7 @@
"\n",
"\n",
"def get_tools(query):\n",
" docs = retriever.get_relevant_documents(query)\n",
" docs = retriever.invoke(query)\n",
" return [ALL_TOOLS[d.metadata[\"index\"]] for d in docs]"
]
},

2
cookbook/langgraph_crag.ipynb
View File

@@ -206,7 +206,7 @@
" print(\"---RETRIEVE---\")\n",
" state_dict = state[\"keys\"]\n",
" question = state_dict[\"question\"]\n",
" documents = retriever.get_relevant_documents(question)\n",
" documents = retriever.invoke(question)\n",
" return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
"\n",
"\n",

2
cookbook/langgraph_self_rag.ipynb
View File

@@ -213,7 +213,7 @@
" print(\"---RETRIEVE---\")\n",
" state_dict = state[\"keys\"]\n",
" question = state_dict[\"question\"]\n",
" documents = retriever.get_relevant_documents(question)\n",
" documents = retriever.invoke(question)\n",
" return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
"\n",
"\n",

2
cookbook/multi_modal_RAG_chroma.ipynb
View File

@@ -435,7 +435,7 @@
" display(HTML(image_html))\n",
"\n",
"\n",
"docs = retriever.get_relevant_documents(\"Woman with children\", k=10)\n",
"docs = retriever.invoke(\"Woman with children\", k=10)\n",
"for doc in docs:\n",
" if is_base64(doc.page_content):\n",
" plt_img_base64(doc.page_content)\n",

2
cookbook/multi_modal_RAG_vdms.ipynb
View File

@@ -443,7 +443,7 @@
"\n",
"\n",
"query = \"Woman with children\"\n",
"docs = retriever.get_relevant_documents(query, k=10)\n",
"docs = retriever.invoke(query, k=10)\n",
"\n",
"for doc in docs:\n",
" if is_base64(doc.page_content):\n",

2
cookbook/rag_semantic_chunking_azureaidocintelligence.ipynb
View File

@@ -168,7 +168,7 @@
"\n",
"retriever = vector_store.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n",
"\n",
"retrieved_docs = retriever.get_relevant_documents(\"<your question>\")\n",
"retrieved_docs = retriever.invoke(\"<your question>\")\n",
"\n",
"print(retrieved_docs[0].page_content)\n",
"\n",

2
cookbook/self_query_hotel_search.ipynb
View File

@@ -1227,7 +1227,7 @@
}
],
"source": [
"results = retriever.get_relevant_documents(\n",
"results = retriever.invoke(\n",
" \"I want to stay somewhere highly rated along the coast. I want a room with a patio and a fireplace.\"\n",
")\n",
"for res in results:\n",